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Experiment --------------- vs. observation study -------------
- (imposes a treatment)
- (collect and analyze w/o change)
Can help determine cause and effect
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Confounding factor-
a variable in an experiment that was not anticipated before an experiment, but is known now (slope in fertilizer experiment)
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correlation association
find definition
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correlation does not mean
causation
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Hypothetico-deductive reasoning
The Scientific Method
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Experimental design:
(1) Replicate—(2) Assign treatments at random.(3) Statistical analysis is used to determine significant effects.
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As the number of replicates increases, it becomes less likely that the
results were actually due to a variable that was not measured or controlled.
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Assigning treatments at random helps to limit
the effects of unmeasured variables.
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Three Basic Principles of Experimental Design
- The treatment is applied independently to the experimental unit(s)
- 2 EUs)The treatment is randomly applied.
- The treatment is replicated…in space and time.
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Stastics are
a way to quantify uncertainty
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Statistics deals with
- Data collection
- Summarizing the data
- Placing data into some context
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Statistics allows a scientist to make sense of ----------------- and to test -------------
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a sample is a
subset of a much larger population
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Two tailed test
two sided If the sample that is being tested falls into either of the critical areas, the alternative hypothesis will be accepted instead of the null hypothesis.(Ie boys may be smarter or girl may be)
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Dispersion
(standard error, standard diviation, variance, rangemeasures values outside of mean)
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Measures of central tendency examples
(mean median mode)
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variance-
the degree to which values deviate from the mean
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The more deviation from the mean, the greater the degree of --------- in the data
“spread”
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Alternative hypothesis (Ha)-----
the hypothesis that represents a change or an effect
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Decision rule:
a rule for deciding whether or not to reject the null hypothesis
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Null hypothesis (Ho):
the hypothesis that represents no change or no effect
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Error-
When our hypothesis is wrong
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Type I error is erroneously saying things are
different when in fact they are not.
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S2 p
is the pooled variance; assumes both populations have equal variances
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In order to compare the 2 samples (female vs. male) we need to
- -Compute the t statistic based on our data.
- -Determine the degrees of freedom.
- -Set the level of significance (α).
- -Compare t-calculated statistic to t-critical statistic.
- -If tcalculated > tcritical, REJECT H0
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